Pre-diagnostic loss to follow-up in an active case-finding TB program: a mixed-methods study from rural Bihar, India
Bibliographic record
Abstract
ABSTRACT background Despite active case-finding (ACF) identifying more presumptive and confirmed TB cases, high pre-diagnostic loss to follow-up (PDLFU) among presumptive TB cases referred for diagnostic test remains a concern. We aimed to quantify the PDLFU, and identify the barriers and enablers in undergoing a diagnostic evaluation in an ACF program implemented in 1.02 million rural population in the Samastipur district of Bihar, India. methods During their routine work, Accredited Social Health Activists (ASHA, a community health worker or CHW), informal providers, and community laypersons identified people at risk of TB, and referred them to the program. A field coordinator (FC) screened them for TB symptoms at the patient’s home. The identified presumptive TB cases were accompanied by the CHW to a designated government facility for diagnostics. Those with a confirmed TB diagnosis were put on treatment by the CHW and followed-up till treatment completion. All services were provided free of cost and patients were supported throughout the care pathway, including a transport allowance. We analyzed programmatically collected data, conducted in-depth interviews with patients, and focus group discussions with the CHWs and FCs in an explanatory mixed-methods design. results A total of 11146 presumptive TB cases were identified from January 2018 to December 2018, out of which 4912 (44.1%) underwent a diagnostic evaluation. The key enablers were CHW accompaniment and support in addition to the free TB services in the public sector. The major barriers identified were transport challenges, deficient family and health provider support, and poor services in the public system. conclusion If we are to find missing cases, the health system needs urgent reform, and diagnostic services need to be patient-centric. A strong patient support system engaging all stakeholders and involvement of CHWs in routine TB care is an effective solution. STRENGTHS AND LIMITATIONS OF THIS STUDY First such study to explore the reasons for pre-diagnostic loss to follow-up A mixed-method design including the views of both patients and community health workers Uses operational data from a routine programmatic setting at an NGO site No record of the actual number of people screened intuitively before being referred to the program. No record of patients accessing diagnostics in private sector and those completing the diagnostic process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".